In 2025, entrepreneurs will unleash a flood of AI-powered apps. Finally, generative AI will deliver on the hype with a new crop of affordable consumer and business apps. This is not the consensus view today. OpenAI, Google, and xAI are locked in an arms race to train the most powerful large language model (LLM) in pursuit of artificial general intelligence, known as AGI, and their gladiatorial battle dominates the mindshare and revenue share of the fledgling Gen AI ecosystem. For example, Elon Musk raised 6 billion to launch the newcomer xAI and bought 100,000 Nvidia H100 GPUs, the costly chips used to process AI, costing north of 3 billion to train its model, Grok.

Language-based AI systems are diffusing into society, bringing positive and negative impacts. Mitigating negative impacts depends on accurate impact assessments, drawn from an empirical evidence base that makes causal connections between AI usage and impacts. Interconnected post-deployment monitoring combines information about model integration and use, application use, and incidents and impacts. For example, inference time monitoring of chain-of-thought reasoning can be combined with long-term monitoring of sectoral AI diffusion, impacts and incidents. Drawing on information sharing mechanisms in other industries, we highlight example data sources and specific data points that governments could collect to inform AI risk management.

Many applications must provide low-latency LLM service to users or risk unacceptable user experience. However, over-provisioning resources to serve fluctuating request patterns is often prohibitively expensive. In this work, we present a best-effort serving system that employs deep reinforcement learning to adjust service quality based on the task distribution and system load. Our best-effort system can maintain availability with over 10x higher client request rates, serves above 96% of peak performance 4.1x more often, and serves above 98% of peak performance 2.3x more often than static serving on unpredictable workloads. Our learned router is robust to shifts in both the arrival and task distribution. Compared to static serving, learned best-effort serving allows for cost-efficient serving through increased hardware utility. Additionally, we argue that learned best-effort LLM serving is applicable in wide variety of settings and provides application developers great flexibility to meet their specific needs.

Recent explorations with commercial Large Language Models (LLMs) have shown that non-expert users can jailbreak LLMs by simply manipulating the prompts; resulting in degenerate output behavior, privacy and security breaches, offensive outputs, and violations of content regulator policies. Limited formal studies have been carried out to formalize and analyze these attacks and their mitigations. We bridge this gap by proposing a formalism and a taxonomy of known (and possible) jailbreaks. We perform a survey of existing jailbreak methods and their effectiveness on open-source and commercial LLMs (such as GPT 3.5, OPT, BLOOM, and FLAN-T5-xxl). We further propose a limited set of prompt guards and discuss their effectiveness against known attack types.

AI has been heralded as the new "brains" for software applications, a role long held by databases. Unfortunately, AI is not so easy for application developers and operations teams to adopt and absorb. Actually, incorporating machine-learning models (which power AI) in productivity-focused applications -- to make them smarter -- is overly difficult and complex. Moreover, ML models depend on specific combinations of hardware and software infrastructure. Without the right infrastructure, the models either cannot perform well enough to be viable or, in some cases, become prohibitively costly.

As an increasing number of organizations drive AI-powered digital transformation, several key trends in operationalizing AI are emerging. Growth leaders are separating themselves from growth laggards by using AI and machine learning (ML) in modern application development. Below are some statistics provided by 451 Research: Leaders invest in models for digital transformation: More than half the digital transformation leaders adopted ML compared to less than 25 percent of laggards. Furthermore, 62 percent of enterprises are developing their own models. Prevalence of DevOps increases the demand for automation: 94 percent of enterprise companies have now adopted DevOps. Models are becoming integral to the development of enterprise apps—requiring continuous, synchronized and automated development and deployment lifecycles. Data science and DevOps/app teams collaborate more: In 33 percent of enterprises, the data science/data analytics team is the primary DevOps stakeholder. An increasing number of application developers are becoming interested in data science and AI, and many have already learned the fundamentals…

An AI-focused neural network software engineer walks into a data shop says hello to the shopkeeper. "I'll have two data preparation functions, one testing and debugging toolset, a couple of application log tracking systems and a bag of potatoes," asks the engineer. Okay it's not a great joke, there's no punchline and the potatoes part is definitely just a ruse, but the way we might build the Artificial Intelligence (AI) functions of tomorrow has a kind of composabe, package-able feel. If it's not quite off-the-shelf AI, then its composable AI that brings together some of the core functions that smart systems use regularly. It's still down to our neural network engineer to know the recipe and peel the spuds, but we can start to shop for many of the individual components needed now.

The impact of artificial intelligence on software engineering and technology companies is undeniable and continues to increase. Many organizations are using this revolutionary technology to create powerful Web and mobile applications out of the box. Regardless of size, companies can use AI to increase ROI, increase efficiency, and greatly reduce operational risk. Large companies (companies with at least 100,000 employees) are most likely to benefit from artificial intelligence strategies, but only half of the companies (sources). About 47% of digitally mature companies say they have a clear artificial intelligence strategy (source).

Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services and products to users, and large technology firms enable this via RESTful APIs. While such APIs promise an easy-to-integrate on-demand machine intelligence, their current design, documentation and developer interface hides much of the underlying machine learning techniques that power them. Such APIs look and feel like conventional APIs but abstract away data-driven probabilistic behaviour - the implications of a developer treating these APIs in the same way as other, traditional cloud services, such as cloud storage, is of concern. The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision. We use Stack Overflow to mine indications of the frustrations that developers appear to face when using computer vision services, classifying their questions against two recent classification taxonomies (documentation-related and general questions). We find that, unlike mature fields like mobile development, there is a contrast in the types of questions asked by developers. These indicate a shallow understanding of the underlying technology that empower such systems. We discuss several implications of these findings via the lens of learning taxonomies to suggest how the software engineering community can improve these services and comment on the nature by which developers use them.

Over the last few years, the technology market has witnessed many new and emerging technologies that are making their way straight into the mainstream industries. Among this Artificial Intelligence is one of the technologies which is contributing more to every industry. The exciting fact about AI is, This uses human-level intelligence with the concept of machine learning. These are a few of many revenue-generating proportions of artificial intelligence that are also contributing to the enterprise mobile app development market. Artificial Intelligence is making many machines capable of learning and interacting in a manner similar to that of human beings.